|By Robert Eve||
|November 15, 2011 10:00 AM EST||
Dynamic Times Require Dynamic BI
Today's IT organizations face the daunting task of responding to constantly changing business demands, many of which require timely development of new or revised IT solutions.
Mergers and acquisitions are just one example.
Another is the fact that supply chains must form and re-form seemingly overnight as product lifecycles shorten and products become more personalized.
Further, with the explosion of social media and mobile computing, end users are adding new and unforeseen demands for fast access to information.
Has IT's mission to support business's dynamic information needs become an impossible quest?
Business No Longer Waits for IT
In true Darwinian fashion, the business side of most organizations is now taking greater responsibility for fulfilling its own information needs rather than depending solely on already-burdened IT resources.
For example, in a 2011 survey of over 625 business and IT professionals entitled Self-Service Business Intelligence: TDWI Best Practices Report, @TDWI July 2011, The Data Warehousing Institute (TDWI) identified the following top five factors driving businesses toward self-service business intelligence:
- Constantly changing business needs (65%)
- IT's inability to satisfy new requests in a timely manner (57%)
- The need to be a more analytics-driven organization (54%)
- Slow and untimely access to information (47%)
- Business user dissatisfaction with IT-delivered BI capabilities (34%)
What Is IT's New Role?
As the business takes greater ownership of its information needs, how does IT's role change?
In the same survey report, authors Claudia Imhoff and Colin White suggest that IT's focus shifts toward making it easier for business users "to access the growing number of dispersed data sources that exist in most organizations."
Examples Imhoff and White cite include:
- providing friendlier business views of source data
- improving on-demand access to data across multiple data sources
- enabling data discovery and search functions
- supporting access to other types of data, such as unstructured documents; and more.
Given today's information technology challenges, it is time for both IT and the business to move beyond maintaining the status quo and explore together new options to meet increasingly demanding needs for information.
An Evolving BI and Data Integration Landscape
Over the past fifty years, business use of information systems has expanded from the initial automation of financial accounting functions, such as general ledger and accounts payable, to enterprise-wide business process automation solutions such as ERP, CRM, HCM, SCM and more.
With core business processes systemized, BI solutions were a natural follow-on. These solutions enabled business users to leverage the data assets locked inside transaction systems to improve business decision agility and overall business performance. However, transaction system architectures were optimized for transaction processing, not for the heavy duty query requirements inherent in BI reporting and analysis solutions.
As a result, BI solutions were based on a different architectural paradigm. In this architecture, BI reporting and analysis applications displayed information to business users. Data integration and data management solutions prepared the data behind the scenes.
To support this architecture, new data integration middleware technologies, such as extract, transform and load (ETL), data replication and data propagation, were developed and adopted. And as a complement to this data integration middleware, new data management solutions - e.g., data warehouses, data marts and cubes - emerged to store, manage and deliver the integrated and consolidated data necessary to support BI.
Advantages Of Traditional Approaches
There are many advantages to adopting these now-traditional data integration and data management approaches. The most important is that they enable businesses to successfully meet increasingly complicated information needs.
In fact, an entire ecosystem has formed around these approaches to satisfy functionality requirements and reduce risk.
- Technology vendors provide powerful tools.
- Organizations such as The Data Warehouse Institute (TDWI) and the Data Management Association (DAMA) provide education and document best practices.
- Services firms provide external resources to complement internal IT staff.
Disadvantages Must Also Be Considered
However, there are two major disadvantages to these traditional approaches. The first disadvantage is the extended time it takes to develop solutions that meet new information requirements and to adapt existing solutions. Because of their inherent architectural complexity, using traditional data integration approaches to support new or changed business needs has typically resulted in long lead times and seemingly endless backlogs.
Business dissatisfaction with this slow pace of change, or time to solution, is evidenced in the TDWI survey results shown above. Clearly, this constraint on IT responsiveness is suboptimal in a dynamic business environment that demands new solutions quickly.
The second disadvantage of using traditional data integration approaches is lack of resource agility. These approaches require design and development in three distinct technologies - BI, data warehousing and ETL. Creating and coordinating metadata, data models, objects and more across these tools is people intensive.
Further, replicating data into a data warehouse and/or mart necessitates additional infrastructure and governance resources to effectively manage multiple copies of data. Balancing these resource-intensive efforts against financial constraints often means fewer resources are available to meet new business needs.
Data Virtualization Addressed Agility Needs
To fulfill rapidly-expanding and ever-changing information needs on the business side and at the same time increase time-to-solution and resource agility on the IT side, a new approach to data integration, called data virtualization, has evolved with wide adoption over the past ten years.
Data virtualization is a data integration technique that provides complete, high-quality and actionable information through virtual integration of data across multiple, disparate internal and external data sources. Data virtualization is implemented using middleware technology that connects to data sources, executes queries to retrieve requested data, combines or federates this data with other data, abstracts and transforms the data to conform to the business information need and then delivers the data to the consuming application.
Contrasting Data Virtualization with Traditional Data Integration
Perhaps the easiest way to understand data virtualization is to contrast it with traditional data integration.
Instead of copying and moving existing source data into physical, integrated data stores (e.g., data warehouses and data marts), as is done with traditional data integration approaches, data virtualization creates a virtual or logical data store. In other words, data virtualization leaves source data in place and uses a set of virtual views and data services to access, integrate, represent and deliver the data to business users and applications.
How Data Virtualization Provides the Agility BI Requires
By significantly improving business decision agility, time-to-solution agility and resource agility, data virtualization provides enterprises can enable self-service BI more successfully and sooner than through traditional data integration methods alone.
- Data virtualization delivers the complete high-quality, actionable information required for agile business decision making.
- Data virtualization uses a streamlined approach, an iterative development process, and ease of change to significantly accelerate IT time to solution.
- And finally, data virtualization directly enables greater resource agility through superior developer productivity, lower infrastructure costs, and better optimization of data integration solutions.
Self-service BI is business's natural response to a fast moving business environment where traditional IT cannot keep pace. Data virtualization is a data integration approach and technology that can enable self-service BI more successfully and sooner.
But don't just take my word for it. For a look at how ten large enterprises across a range of industries and domains are successfully doing it as well, go to www.datavirtualizationbook.com.
- Five Ways Data Virtualization Improves Data Warehousing
- Data Virtualization Technology Advancements Deliver New Value
- Data Virtualization at Pfizer: A Case Study
- Why Bother to Abstract Your Data?
- Data Virtualization Adoption Propelled by Significant Business Benefits
- Extend MDM with Data Virtualization
- Will Data Virtualization Work for Me?
- Can Virtualization Help with Governance?
- Data Virtualization Reaches Critical Mass
- Roadmap for Data Virtualization
- It’s Here! The First Book on Data Virtualization
- How to Evaluate a Data Virtualization Platform
- The EDW Is Dead! Is Data Virtualization the Crown Prince?
- Why Energy Companies Like Data Virtualization
- When Should We Use Data Virtualization?